Diagnósticos de enfermería del Dominio de Seguridad/protección en pacientes en postoperatorio
Bibliographic record
Abstract
Introduccion: el periodo postoperatorio (PO) es una fase critica que demanda cuidados redoblados de todo el equipo de sanidad, sobre todo del equipo de enfermeria. Objetivo: caracterizar los diagnosticos de enfermeria del Dominio Seguridad/ proteccion en pacientes en periodo de postoperatorio en un hospital universitario en Natal, Rio Grande do Norte. Metodos: estudio descriptivo de tipo transversal. Los datos fueron recogidos entre octubre y diciembre de 2012. Para la investigacion se utilizo un protocolo de recogida de datos y el examen fisico basado en la taxonomia NANDA-I. Resultados: de los 80 pacientes, 60,0 % eran del sexo masculino, con una media de 47,46 anos de edad. Se encontro mayor relevancia en las cirugias abdominales (70 %) y se destaca que el 45 % de los pacientes presentaba un cuadro de infeccion. Los diagnosticos de enfermeria del Dominio de Seguridad/ proteccion que presentaron una frecuencia mayor al 50 % fueron: Riesgo de caidas (86,3 %), Problemas de denticion (71,3 %) y el Riesgo de infeccion (55 %). En este contexto, los enfermeros deben planear los cuidados considerando los aspectos de seguridad y proteccion para los pacientes en periodo postoperatorio.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".